ArticleNPJ systems biology and applications2022
Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box.
Article in NPJ systems biology and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Leveraging neural network models for drug repurposing: a case study on cardiac hypertrophy.Scientific reports · 2026Article
- Transcriptional mechanisms of sex-biased gene expression and their connections to disease-associated variation.Human molecular genetics · 2026Article
- The human metabolome and machine learning improves predictions of the post-mortem interval.Nature communications · 2026Article
- Gene expression inference based on graph neural networks using L1000 data.Briefings in bioinformatics · 2025Article
- Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.Patterns (New York, N.Y.) · 2024Article
- CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.ACS synthetic biology · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.bioRxiv : the preprint server for biology · 2024Article
- Effective Pruning for Top-k Feature Search on the Basis of SHAP Values.IEEE access : practical innovations, open solutions · 2024Article
- Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations.Bioinformatics (Oxford, England) · 2023Article
- An interpretable model for stock price movement prediction based on the hierarchical belief rule base.Heliyon · 2023Article
- Interspecific comparison of gene expression profiles using machine learning.PLoS computational biology · 2023Article
- Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities.Chemical research in toxicology · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). We find that the expression of 1600 TFs can explain >95% of the variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an over-representation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the dysregulation of the target genes (rho = 0.61, P < 10
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.